code: def __init__(self, input_dim, dim=1, total_dim=4, eps=1e-5):
      super().__init__()
      self.dim = dim if dim >= 0 else total_dim + dim
      param_size = [
          1 if ii != self.dim else input_dim for ii in range(total_dim)
      ]
      self.gamma = nn.Parameter(
          torch.Tensor(*param_size).to(torch.float32)
      )
      self.beta = nn.Parameter(
          torch.Tensor(*param_size).to(torch.float32)
      )
      nn.init.ones_(self.gamma)
      nn.init.zeros_(self.beta)
      self.eps = eps
  
imports: import warnings
  import torch
  import torch.nn
  from espnet2.enh.layers.dptnet import ImprovedTransformerLayer
  from espnet2.enh.layers.tcn import ChannelwiseLayerNorm
  from espnet2.torch_utils.get_layer_from_string import get_layer
label_desc: Use image node_id 1 to create a new LayerNormalization object from inherited base classes: nn with example: obj = LayerNormalization(input_dim, dim, total_dim, eps)